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Data Visualization with Python

This Data Visualization course covers a wide range of advanced data visualization techniques, including advanced visualization libraries like Matplotlib, Seaborn, and Plotly, creating interactive dashboards using Plotly Dash, and integrating visualizations into web applications.

Industrial Trainers Support

Work on Real Time Projects

One-on-one Sessions

Fully Interactive Online Sessions

About Data Visualization course

No of classes X Hours

69 x 3 = 180 Hours

Self Study Hours

120 Hours

Assignments & Case Studies

30

Data Visualization is a critical aspect of data analysis that helps us to interpret complex data and communicate insights to stakeholders effectively and it is designed to take your skills to the next level and equip you with the knowledge and tools to create compelling visualizations using Python.

The course is perfect for anyone with a basic data visualization understanding and wanting to advance their skills. We cover advanced techniques and tools to create professional-grade visualizations that will help you to communicate your data insights clearly and effectively.

Our course covers a wide range of topics, including:

  • Advanced data visualization techniques using Matplotlib, Seaborn, and Plotly
  • Creating interactive dashboards using Plotly Dash
  • Advanced data manipulation using Pandas and NumPy
  • Best practices for designing effective visualizations
  • Integrating visualizations into web applications

Our course is taught by experienced experts in data visualization using Python who provide hands-on training with real-world datasets, so you can practice your skills and apply what you learn in real-life scenarios.

We also provide access to a community of fellow learners where you can share your experiences, get feedback on your work, and collaborate on projects.

Upon completing the course, you will receive a certificate of completion, which you can add to your resume and LinkedIn profile to demonstrate your expertise in advanced data visualization using Python.

Take this opportunity to take your skills to the next level. Enroll in our “Data Visualization using Python” course and gain the skills you need to succeed in today’s data-driven world.

Data Visualization Course Curricullum

This course will cover the intermediate level of tableau dashboard, Power BI dashboard, excel dashboard, ggplot2 dashboard and matplotlib dashboard. The advanced data visualization course will teach us about tools such as Tableau, Power BI, Excel, R, and Python to generate visualizations.
  • Chapter 1.1 : Basic Excel
  • Chapter 1.2 : Basic Programming Elements
  • Chapter 1.3 : Introduction of Basic Statistics
  • Chapter 1.4 : Overview of Dashboard
  • Chapter 1.5 : Business dashboard creations
  • Chapter 1.6 : Data manipulation using functions
  • Chapter 1.7 : Data Visualization in Excel
  • Chapter 1.8 : Introduction to Analytics & Data Science
  • Chapter 1.9 : Create dashboard in Excel – Using pivot controls
  • Chapter 2.1 : Data Analytics with VBA
  • Chapter 2.2: A look at some commonly used code snippets
  • Chapter 2.3 : Programming Constructs in VBA
  • Chapter 2.4 : Functions & Procedures in VBA-Modularizing your programs
  • Chapter 2.5 : Objects & Memory Management in VBA
  • Chapter 2.6 : Error Handling
  • Chapter 2.7 : Controlling accessibility of your code – Access Specifiers
  • Chapter 2.8 : Code reusability – Adding references and components of
    your code
  • Chapter 2.9 : How VBA works with Excel
  • Chapter 2.10 : Key Component of programming language
  • Chapter 2.11 : Communicating with your users
  • Chapter 3.1 : Intro to RDBMS & Basic SQL
  • Chapter 3.2 : Data Based Object Creation (DDL commands)
  • Chapter 3.3: Data Manipulation
  • Chapter 3.4: Accessing data from multiple tables using Select
  • Chapter 3.5: Advanced SQL
  • Chapter 4.1 : Getting Started
  • Chapter 4.2 : Data handlings & Summaries – I
  • Chapter 4.3 : Data handlings & Summaries – II
  • Chapter 4.4 : Building Advanced Reports/Maps
  • Chapter 4.5 : Calculated Fields
  • Chapter 4.6 : Table Calculations
  • Chapter 4.7 : Parameters
  • Chapter 4.8 : Buildings Interactive Dashboard
  • Chapter 4.9 : Building Stories
  • Chapter 4.10 : Work with data
  • Chapter 4.11 : Sharing work with others
  • Chapter 5.1 : Introduction to R
  • Chapter 5.2 : Installation of R Software
  • Chapter 5.3 : Basics of R Programming
  • Chapter 5.4 : Modular Programming – Packages
  • Chapter 5.5 : Data Types & Data structures
  • Chapter 5.6 : Other Programming Elements
  • Chapter 5.7 : Importing & Exporting data
  • Chapter 5.8 : Understanding of data
  • Chapter 5.9 : Data Preparation/manipulation of data
  • Chapter 5.10 : Basic Statistics
  • Chapter 5.11 : Data Visualization
  • Chapter 5.12 : Shiny
  • Chapter 5.13 : Introduction to Modelling
  • Chapter 5.14 : Linear Regression
  • Chapter 5.15 : Logistic Regression
  • Chapter 6.1 : Introduction to the field of Data Science and Python
  • Chapter 6.2 : Data Types and Structures of Core Python
  • Chapter 6.3 : Role of Modular Programming in Python
  • Chapter 6.4 : Vectorized Data Structures: Numpy Array and Pandas Series
  • Chapter 6.5 : Data Mining (Basic)
  • Chapter 6.6 : Data Mining (Intermediate)
  • Chapter 6.7 : Data Mining (Advanced)
  • Chapter 6.8 : Data Quality check
  • Chapter 6.9 : Data Visualization
  • Chapter 6.10 : Descriptive Statistics
  • Chapter 6.11 : Understanding Probability Distribution
  • Chapter 6.12 : Hypothesis Testing
  • Chapter 6.13 : Finding Business Insights using Statistics
  • Chapter 6.14 : Combining Data Mining and Statistics
  • Chapter 6.15 : Introduction to Predictive Modeling
  • Chapter 6.16 : Encoding and Binning
  • Chapter 6.17 : Basics of Regression
  • Chapter 6.18 : Regression Model Building : Pre-Modeling
  • Chapter 6.19 : Regression Model Building : Modeling
  • Chapter 6.20 : Regression Model Building – Post Modeling
  • Chapter 6.21 : Basics of Classification using Logistic Regression

Who Should Do?

Students should be familiar with the programming language basics or be prepared to know Python, Javascript, GitHub, and other computational tools.

Data Visualization FAQ

Course Fee

₹ 35,000
  • Full Lifetime Access
  • Live Interactive Classes​
  • Certificate of completion
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